Community detection in social networks is crucial for understanding the structure and dynamics of networks. It enables the identification of communities on various social platforms, offering applications such as discovering groups with shared interests, delivering targeted content, personalized advertisements, and recommendations. Additionally, it helps in uncovering political or social opinion trends and detecting unusual or fake accounts that do not belong to any community. Despite its importance, community detection remains a challenging task due to the dynamic nature of networks, overlapping relationships and scalability issues. In this paper, we compare five widely used community detection algorithms: Girvan-Newman, Louvain, Clique Percolation, Infomap, and Label Propagation with our proposed enhanced NWFCM algorithm. We used two datasets: the Facebook datasets of Harvard University and the facebook dataset of Columbia University. The refined version of the Non-Weighted Fuzzy C-Means (NWFCM) algorithm performed better for community detection by incorporating fuzziness. The refined NWFCM effectively identifies overlapping communities, addressing limitations of the original algorithm. The refined version of NWFCM offers improved performance, effectively handles diverse network structures, and delivers more accurate and reliable community detection results.